Formation control for a class of nonlinear multiagent systems using model‐free adaptive iterative learning

Abstract

Summary In this paper, the problem of formation control is considered for a class of unknown nonaffine nonlinear multiagent systems under a repeatable operation environment. To achieve the formation objective, the unknown nonlinear agent's dynamic is first transformed into a compact form dynamic linearization model along the iteration axis. Then, a distributed model‐free adaptive iterative learning control scheme is designed to ensure that all agents can keep their desired deviations from the reference trajectory over the whole time interval. The main results are given for the multiagent systems with fixed communication topologies and the extension to the switching topologies case is also discussed. The feature of this design is that formation control can be solved only depending on the input/output data of each agent. An example is given to demonstrate the effectiveness of the proposed method.

Multi-Agent Trajectory-Tracking Flexible Formation via Generalized Flocking and Leader-Average Sliding Mode Control

“…Formation control with multi-agent orientation rolling and shaping is examined by [20], [39]. Formation control under iterative learning can be found in [4], [27], [28]. To achieve flexible or time-varying multi-agent formation, interesting discussions are summarized in [1], [9], [10], [15], [50].…”

Modeling and robust adaptive iterative learning control of a vehicle‐based flexible manipulator with uncertainties

“…Iterative learning control (ILC) is a useful control strategy to make systems operate in repetitive tasks and increase control precision by learning from the previous control experience. [21][22][23][24][25][26] Since ILC can achieve expected output trajectory tracking in a limited operation time and can be employed with no need to know perfect information of the target system, it is widely applied to robotic manipulators. In the work of Tayebi, 27 an adaptive ILC method is developed for rigid robot manipulators to realize trajectory tracking with unknown parameters.…”

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

“…First, the contraction mapping (CM) method, which was the most common technique in the earlier ILC studies, was used in the works of Yang et al for continuous, affine nonlinear systems with both fixed and iteration‐varying graphs. This technique can also be found in the works of Lv et al and Bu et al Moreover, because ILC considers both the time and iteration axes, two‐dimensional (2D) system techniques have also been commonly used for deriving stability results. Such a technique has been addressed in depth in some works .…”

“…34 In contrast to the existing studies on ILC for MASs, this paper features a quite general heterogeneous agent model. Existing literature on ILC for nonlinear MASs can be classified into two categories: unknown nonlinear functions with globally Lipschitz continuous condition [26][27][28][29] and parameterized nonlinear form with known nonlinearities. [33][34][35][36][37][38] For the former case, a CM technique can be employed to analyze the influence of nonlinearities, and for the latter case, an adaptive control technique is useful for control design and analysis.…”